Traffic Injuries Research Articles

Crash risk prediction using sparse collision data: Granger causal inference and graph convolutional network approaches

Applying deep learning techniques in predicting traffic crashes provides a possibility to achieve the vision of “zero fatalities” by implementing a proactive safety countermeasure. The overly sparse distribution of crashes hinders high-precision prediction of crash risks in urban areas. The lack of effective crash association analysis also reduces the interpretability of the prediction model. To address these gaps, this paper proposes a novel method, Priori Spatial and Temporal Causal Graph Convolutional Network (PST-CGCN) for predicting crash risk based on causal inference and graph convolutional network. In PST-CGCN, a data enhancement strategy utilizing historic priori spatial and temporal knowledge is employed to improve the distinguishability between crash data, then a graph convolutional network based on causal association is designed. The focus of this study is to shift attention from using multiple data sources to predict traffic risk to exploring the intrinsic temporal and spatial causal relationship between traffic crashes in different regions. Results from the NYC case study show that PST-CGCN model performs best compared with seven baseline models on three indicators, identifying high crash risk periods and areas in short-term forecasting. The results of ablation experiments on the PST-CGCN demonstrate the rationality of each component in the framework. Besides, gradient analysis combined with causal association is applied to explore directed causal relationships between regions, which enhances the model’s interpretability.

Shifts in Paediatric Road Trauma Dynamics Pre- vs. Post-COVID-19 Lockdown: Insights from a Major Trauma Centre.

The COVID-19 pandemic has brought unprecedented changes globally, significantly affecting public health sectors, including paediatric road trauma. This study aims to explore the changes in paediatric road trauma presentations and outcomes before and after the COVID-19 lockdown. This retrospective study analysed paediatric trauma data from the Saudi TraumA Registry (STAR) from August 2017 to December 2022, comparing pre- and post-COVID-19 lockdown periods (August 2017-March 2020 and July 2020-December 2022, respectively). The study analysed demographic data, mechanism of injury, severity, ICU admissions, and mortality rates using multivariate logistic regression models. Out of 950 paediatric trauma cases analysed, there was an 44.2 [561/389 = 1.442] % increase in the number of cases post-lockdown. A significant shift was noted in the age group of 5-9 years, with cases increasing from post-lockdown. Head injuries were the most prevalent type of injury, with their proportion slightly increasing from 163 (20.5%) pre-lockdown to 248 (23.2%) post-lockdown. The ICU admission were consistent across both periods, while the definitive care mode of arrival post-lockdown showed a notable shift towards private or government ambulances. Our study provides critical insights into the significant impact of the COVID-19 on paediatric road trauma. The observed increase in trauma cases post-pandemic, particularly among younger children and a notable rise in driver-related injuries among adolescents, underscores the profound effect of lockdown measures and subsequent societal changes on paediatric health. Efforts to reduce paediatric traffic injuries require collaboration among parents, educators, healthcare professionals, policymakers, and the community at large.

Multivariate analysis of road crashes involving two-wheelers at Vienna’s roads

In order to determine the critical elements influencing the frequency and severity of two-wheeler-related traffic crashes, this study explores these incidents along eleven years in Vienna. Applying sophisticated multivariate statistical approaches, a comprehensive dataset is used and includes variables about rider demographics, weather conditions, vehicle features, and crash circumstances to reveal intricate correlations and interactions among these elements. Is there significant and distinctive difference based on gender and age with specific conditions under which crashes are occurring influencing different injury degree. Multiple regression undoubtedly points fields for action in statistically based findings providing the most important answer to this research: why there are so many crashes and what is leading cause of injured two-wheelers. The research yields insightful information that politicians and practitioners of road safety may use to improve two-wheeler safety regulations and lower the number of serious injuries and fatalities.

Pattern of Road Traffic Injuries Presenting at a Tertiary Hospital in North-Central Nigeria

Abstract Background: Road traffic injuries (RTIs) constitute a major economic and health challenge globally, particularly in developing countries. Nasarawa state, north-central Nigeria, is heavily motorised due to the use of motorcycles as a popular means of transportation. The study aims to determine the pattern and prevalence of RTIs within the state. Materials and Methods: A 6-year retrospective study was conducted at the Dalhatu Araf Specialist Hospital (DASH) in Lafia, Nasarawa state. Data were obtained from the medical records of patients admitted with RTIs at the hospital and were analysed using the Statistical Package for the Social Sciences version 26. Results: A total of 929 patients with 1307 injuries were reported. There were 518 (55.8%) males and 411 (44.2%) females, with a male-to-female ratio of 1.3:1. The age of the patients ranged from 1–85 years, with a mean age of 30 ± 20 years. Young adults aged 18–44 years constituted the most injured age group. Motorcycle-related crashes accounted for injuries in 530 (57.1%) patients. These injuries were predominantly traumatic brain injuries (TBIs) and extremity injuries. The body region most affected was the head and face (560, 42.8%), with traumatic brain injuries (448, 32.7%) being the most common type. After treatment, most patients (571, 61.5%) were discharged home, while the mortality rate was 2.4%. Conclusion: In our study, motorcycle-related crashes were prevalent. Young adult males were mostly the victims of RTIs. Traumatic brain injuries and extremity injuries were the predominant types of injuries, often associated with motorcycle and pedestrian crashes. Multisystem injuries and TBIs were major factors contributing to mortality.

Road’s mortality prediction for the most vulnerable users in the context of the COVID-19 pandemic in Santiago de Cali, Colombia

The purpose of this study is to predict the frequency of mortality from urban traffic injuries for the most vulnerable road users before, during and after the confinement caused by COVID-19 in Santiago de Cali, Colombia. Descriptive statistical methods were applied to the frequency of traffic crash frequency to identify vulnerable road users. Spatial georeferencing was carried out to analyze the distribution of road crashes in the three moments, before, during, and after confinement, subsequently, the behavior of the most vulnerable road users at those three moments was predicted within the framework of the probabilistic random walk. The statistical results showed that the most vulnerable road user was the cyclist, followed by motorcyclist, motorcycle passenger, and pedestrian. Spatial georeferencing between the years 2019 and 2020 showed a change in the behavior of the crash density, while in 2021 a trend like the distribution of 2019 was observed. The predictions of the daily crash frequencies of these road users in the three moments were very close to the reported crash frequency. The predictions were strengthened by considering a descriptive analysis of a range of values that may indicate the possibility of underreporting in cases registered in the city’s official agency. These results provide new elements for policy makers to develop and implement preventive measures, allocate emergency resources, analyze the establishment of policies, plans and strategies aimed at the prevention and control of crashes due to traffic injuries in the face of extraordinary situations such as the COVID-19 pandemic or other similar events.

Injury risks for different road users in Nepal: a secondary analysis of routinely collected crash data

Background Road traffic injuries are the 12th leading cause of mortality globally. Nepal, one of the least developed countries in the world, has a rapidly rising rate of road traffic-related morbidity and mortality. There is incomplete reporting of traffic crashes in Nepal in the last three World Health Organization global status reports on road safety. Together with poor-quality coding of crashes, essential information to inform prevention interventions or make international comparisons is lacking. Objectives The study aimed to critically analyse routinely collected traffic police data in Makwanpur district, Nepal, to identify the road user groups most likely to be involved and injured in traffic crashes in this part of the country. Design Secondary data analysis. Setting Makwanpur district, Nepal. Data sources Road traffic crash records from the District Traffic Police Office, Makwanpur, over 2 years (April 2017–March 2018 and May 2019–April 2020). Data were extracted from paper-based records onto an electronic database, and the data were analysed descriptively using the Statistical Package for the Social Sciences version 24.0 (IBM Corporation, USA). Results There were 291 crashes recorded in 2017–8 and 435 crashes in 2019–20. Two-wheeled vehicles (motorcycles/scooters) were the most frequently involved in police-reported crashes (35.5%). For most crashes (419/726, 58%), no cause was documented in the police record. Road users involved in crashes were mostly riders or passengers of motorised two-/three-wheelers (33.4%), four-wheeled vehicles (19.9%) or buses (16.7%). Of fatal injuries, 31.2% were riders or passengers of motorised two-/three-wheelers, and 24.8% were pedestrians. Limitations The data used represent crashes reported to the traffic police and therefore underestimate the true number of crashes and injuries. At the end of the second period of study, local movement restrictions associated with the COVID-19 pandemic lockdown limited the traffic on the roads, resulting in fewer crashes in March and April 2020. In most crash records, there were missing data, limiting the ability to determine contributory factors with confidence. Conclusion This study contributes to the evidence gap by describing crashes and mortality data by vehicle type and road user category. The findings suggest that the number and rates of crashes may be increasing over time. Action to improve the quality and completeness of data routinely collected by the traffic police is needed; a piloted web-based Road Accident Information Management System has the potential to support improved routine data collection. Future work The data indicate crashes occurring in one district over 2 years. To better understand the apparent increase in crashes, an analysis over a longer period, and over multiple districts, would be necessary. Funding This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Global Health Research programme as award number 16/137/49.

Assessment of awareness and knowledge of Good Samaritan Law in the general literate population - A cross-sectional study in Telangana.

Road traffic injuries are the eighth leading cause of death in India. The Law Commission of India report says that 50% of the victims who died of preventable injuries could have been saved had they received the medical care on time. To protect the people who help road accident victims, Good Samaritan Law was enacted. To assess awareness and knowledge about Good Samaritan Law among the general literate population and to assess the impact of awareness session on awareness score. In this cross-sectional study, adult participants of either gender, literate enough to read and comprehend the material provided, were included in the study through either an electronic or written informed consent process. The participants were asked to complete a questionnaire, watch/read the awareness video/material provided, and then complete the post-awareness session questionnaire. The participants were divided into the aware and unaware groups for further assessment of pre-awareness scores. The pre- and post-awareness session scores were compared in the participants using paired-t-test. In this study, 379 participants gave consent for participation; however, only 288 participants attempted the post-awareness questionnaire. The mean pre-awareness session score was significantly higher in the aware group (8.5) as compared to the unaware group (5.6). The pre-awareness scores were significantly high in doctors as compared to medical students in the aware group and as compared to medical students, graduates, teachers, and general public in the unaware group. Post-awareness session scores (9.2 ± 2.8) were observed to be significantly higher (p < 0.001) than pre-awareness session scores (6.4 ± 3.4). In this study, it was observed that the percentage of participants who were aware of the law was less. It demonstrated the impact of awareness session on increasing GSL awareness score in participants. These findings indicate the need of awareness programs to transform bystanders into good samaritans.

Comprehensive analysis of traumatic rupture of the aorta in road traffic crashes: incorporating epidemiological insights and K-prototype clustering

Objectives The identification of crash characteristics associated with traumatic rupture of the aorta (TRA) can significantly enhance countermeasures against TRA. Conventional epidemiological approaches struggle to adequately handle the substantial variability of traffic crash data. Consequently, this study aims to integrate conventional epidemiological analysis with data-driven cluster analysis to more comprehensively analyze TRA-related crash characteristics. Methods A total of 350 unweighted TRA crashes were extracted from traffic crash databases including comprehensive crash details and injury descriptions. Initially, a selection was made of 11 continuous variables and 9 categorical variables, describing crash characteristics. After correlation analysis and principal component analysis were applied to the dataset, K-prototype clustering was finally conducted using 6retained categorical variables and 6 principal components derived from the continuous variables. Results This study found significant age and gender disparities among TRA victims, with 50% falling within the age range of 25–59 years and an overwhelming majority (62.2%) being males. Side impacts emerged as the primary cause of TRA-related crashes (37.2%), followed by collisions with off-road objects (28.6%) and head-on collisions (24.8%). Cluster analyses revealed 6 distinct clusters within the TRA-related crash dataset. These clusters were characterized by factors such as vehicle model year, curb weight, collision dynamics, and seatbelt usage, providing a deeper understanding of the heterogeneity in TRA incidents and their associated factors. Conclusions Although limitations related to available data sources and factors such as accompanying injuries and vehicle weight warrant further comprehensive investigations in the future, this study contributes valuable insights into TRA analysis to enhance understanding and prevention strategies.

Road traffic crash data in Southwestern Nigeria: a qualitative analysis of sources, contents and management methods

Objective This study qualitatively examined road traffic crash (RTC) data collection and management in southwest Nigeria, with the goal of addressing the tenets of the UN Decade of Action Plan on Road Safety’s call for an effective data system. Methods Data on RTCs data collection in the study area was obtained through key informant interviews with five hospital accident emergency unit directors, three Federal Road Safety Commission (FRSC) Sector commanders, and three Nigeria Police Force (NPF) commissioners. Datasets were transcribed, categorized, and interpreted using content analysis, descriptive statistics, and WHO recommended minimal crash data element requirements. Results The study found disparate systems, a lack of synergy, and discrepancy in the various data sources in the country when compared with WHO recommendations on road traffic data collection systems, with 55% of agencies using non-standardized paper forms. The study also reveals that the minimum crash data elements recommended by WHO are not consistently captured in the NPF, FRSC, and hospital traffic crash data sources. Conclusions The study suggested the use of an upgraded National Road Traffic Crash Data Management System (NRTCDMS) Data Template to unify data gathering and linkage issues, but recommends a digital version of the template.

Considering the Reliability of Police-Reported Weather Information on Freeways Traffic Crash Severity Analysis: Proposing a Mixed Statistical and Geospatial Solution

Considering the Reliability of Police-Reported Weather Information on Freeways Traffic Crash Severity Analysis: Proposing a Mixed Statistical and Geospatial Solution

Substance use and driver fatality in Norway: An expanded case-control study.

Using alcohol or psychoactive drugs before driving a motor vehicle may increase the risk of crash involvement, injury, and death. This is better documented for alcohol than for drugs. The aim of this study was to expand a previous case-control study on substance use and driver fatality by doubling the number of cases and controls, and hence improve the statistical power and enable the analysis of combined substance use. We collected data on alcohol and drug use from all 1197 drivers of cars and vans who were fatally injured in road traffic crashes in Norway between 2005 and 2020 ('cases') by analyzing blood samples or reviewing other information on substance use. We also collected data on alcohol and drug use among 17,219 drivers in random road traffic ('controls') by analyzing oral fluid samples. Substance use was converted to dichotomous variables (no use/use). We used unconditional logistic regression to estimate adjusted odds ratios (aORs) with 95% confidence intervals (CIs) of driver fatality for mutually exclusive substance groups, adjusted for sex, age, geographic region, urban centrality class, and time interval of the week. Compared to no substance use, the aOR (95% CI) for driver fatality was for alcohol 91 (61-137), stimulants (primarily amphetamines) aOR 22 (9-56), benzodiazepines and z-hypnotics (BZDs) aOR 4.0 (2.7-5.9), tetrahydrocannabinol (THC) aOR 3.4 (1.7-6.7), and opioids aOR 1.4 (0.4-4.9). The aOR for any polysubstance use was 168 (96-297). The combinations of BZDs with stimulants or THC were associated with markedly higher aORs for driver fatality than the use of single substance groups. Alcohol and polysubstance use are the most important predictors of fatal injury, followed by stimulants.

A new spatiotemporal convolutional neural network model for short-term crash prediction

AbstractPredicting short-term traffic crashes is challenging due to an imbalanced data set characterized by excessive zeros in noncrash counts, random crash occurrences, spatiotemporal correlation in crash counts, and inherent heterogeneity. Existing models struggle to effectively address these distinct characteristics in crash data. This paper proposes a new joint model by combining the time-series generalized regression neural network (TGRNN) model and the binomially weighted convolutional neural network (BWCNN) model. The joint model aims to capture all these characteristics in short-term crash prediction. The model was trained and tested using real-world, highly disaggregated traffic data collected with inductive loop detectors on the M1 motorway in the UK in 2019, along with crash data extracted from the UK National Accident Database for the same year. The short-term is defined as a 30-min interval, providing sufficient time for a traffic control center to implement interventions and mitigate potential hazards. The year was segmented into 30-min intervals, resulting in a highly imbalanced data set with over 99.99% noncrash samples. The joint model was applied to predict the probability of a crash occurrence by updating both the crash and traffic data every 30 min. The findings revealed that 75.3% of crashes and 81.6% of noncrash events were correctly predicted in the southbound direction. In the northbound direction, 78.1% of crashes and 80.2% of noncrash events were accurately captured. Causal analysis and model-based interpretation were used to analyze the relative importance of explanatory variables regarding their contribution to crashes. The results reveal that speed variance and speed are the most influential factors contributing to crash occurrence.

Risk and Protective Factors for Driving Under the Influence of Alcohol, Marijuana, and Both Substances in a Population-Based Sample of High School Students

Using results from the 2017 Nevada Youth Risk Behavior Survey, this research aims to understand factors associated with driving under the influence of alcohol (DUIA), marijuana (DUIM), and both alcohol and marijuana (DUIB) among students who reported they drove and used alcohol and/or marijuana recently. DUIM was the most prevalent behavior (16.4%), followed by DUIB (9.3%) and DUIA (4.9%). Weighted multinomial logistic regression analysis showed more risk and protective factors were associated with DUIB than DUIM and DUIA. To decrease motor vehicle crashes among adolescents, interventions should address modifiable factors for driving under the influence of both alcohol and marijuana.

The impact of COVID-19-related national lockdowns on alcohol-related traffic collisions, injuries, and fatalities in Lithuania.

The national lockdowns that occurred all over the world in response to the Coronavirus Disease 2019 (COVID-19) pandemic have been found to have impacted alcohol use. The aim was to evaluate the impact of COVID-19-related national lockdowns on alcohol-related traffic collisions, injuries, and fatalities in Lithuania. Using monthly data from the Lithuanian Road Police Service for January 2004 to December 2022, we performed interrupted time-series analyses using a generalized additive model to evaluate the impact of COVID-19-related national lockdowns on alcohol-related traffic collisions, injuries, and fatalities. In Lithuania, the COVID-19-related lockdowns occurred from March 2020 to June 2020 and from November 2020 to June 2021. Although overall rates for traffic collisions and injuries decreased during the COVID-19-related lockdowns in Lithuania, these lockdowns were associated with a 3.21% (95% CI: 1.19%, 5.23%) increase in the relative proportion of alcohol-related traffic collisions and a 2.46% (95% CI: 0.12%, 4.80%) increase in the relative proportion of alcohol-related traffic injuries. The association between the lockdowns and alcohol-related traffic fatalities was not statistically significant. The COVID-19-related national lockdowns in Lithuania were associated with a decrease in the overall rate of traffic collisions and injuries, but an increase in the relative proportion of alcohol-related traffic collisions and injuries.

Failed primary repair of blunt duodenal injury managed by tube duodenostomy, gastrojejunostomy and a feeding jejunostomy: a case report

BackgroundThe worldwide increase in road traffic crashes and use of firearms has increased the incidence of duodenal injuries. Upper gastrointestinal radiological studies and computed tomography (CT) in resource settings may lead to the diagnosis of blunt duodenal injury. Exploratory laparotomy remains the ultimate diagnostic test if a high suspicion of duodenal injury continues in the face of absent or equivocal radiographic signs. Although the majority of duodenal injuries may be managed by simple repair, high-risk duodenal injuries are followed by a high incidence of suture line dehiscence and should be treated by duodenal diversion.Case presentationWe report a case of a failed primary repair of a blunt injury to the second part of the duodenum (D2) in a 24-year-old African man. This was successfully managed by a tube duodenostomy, a bypass gastrojejunostomy and a feeding jejunostomy in a low resource setting.ConclusionsDetailed knowledge of the available operative choices in duodenal injury and their correct application is important. When duodenal repair is needed, conservative repair techniques over complex reconstructions should be utilised. The technique of tube duodenostomy can be successfully applied to cases of large defects in the second part of the duodenum (D2), failed previous repair attempts and with defects caused by different aetiology. It may remain especially useful as a damage-control procedure in patients with multiple injuries, significant comorbidities and/or haemodynamic instability.

The effects of weather factors on road traffic casualties: Analysis on provincial panel data of China from 2006 to 2021

IntroductionRoad traffic injuries stands as a major concern globally, as they result in significant loss of life, economic impact, and erode trust in government and societal safety. The influence of weather on road traffic safety is undeniable, impacting road conditions, individuals, and vehicles. However, the specific influence of weather on road traffic casualties has seldom been explored. MethodThis study assesses the effect of weather factors on road traffic casualties in China from 2006 to 2021. Vector error correction models (VECM) were utilized to determine the Granger causality between weather factors and covariates. Furthermore, panel autoregressive distribution lag models (ARDL) were applied to quantify the association between weather factors and road traffic casualties. ResultsThe findings indicate that rainfall and temperature exert a short-term negative impact on casualty risk, which intriguingly becomes positive in the long term. A standout discovery is the significant role of health investments, which are shown to reduce casualty numbers in both the short and long-terms. In the long run, the gross domestic product significantly enhances casualties, while expressway mileage notably decreases them. ConclusionsThese results demonstrate the significant influence of weather on road traffic casualties and highlight the critical roles played by factors such as gross domestic product, health investment, and expressway mileage. The evidence presented in the study underscores the urgent need for more effective strategies to mitigate road traffic casualties. Thus, some effective measures are proposed to reduce road traffic casualties. This study is conducive to the improvement of traffic in severe weather in China and provides guidance for traffic management departments.

Combination of macroscopic and microscopic crash prediction models with multiple modeling approaches: A highway case study

Road traffic crashes are a global problem and represent a major cause of mortality among children and young people worldwide. Crash prediction models (CPM) play a crucial role in identifying crash-prone areas by predicting the expected number of crashes, thus facilitating the implementation of proactive measures. Previous studies have explored various CPM based on spatial analysis or modeling approaches. Nonetheless, a substantial gap still exists in exploring CPM that simultaneously address the combination of spatial units and modeling approaches, using of heterogeneous data at both the macroscopic and microscopic levels to develop models using Multi-Criteria Decision-Making and Statistical methods. Thus, the objective of this study is to develop multiple CPM using various spatial units of analysis and employing different modeling approaches, combine them, and conduct comparisons to determine the final model that best fits the data. A total of 7 models have been developed: three at the macroscopic level, utilizing Analytic Hierarchy Process (AHP), Negative Binomial (NB), and a combination of both; one at the microscopic level using Geographic Weighted Poisson Regression (GWPR). Additionally, three combined models bring together the previous from both levels, resulting in AHP+GWPR, NB+GWPR, and AHP+NB+GWPR models. These combined and individual models have been compared, and the best-performing has been selected through a validation process. The models were calibrated using data from a case study of a highway located in the northwest of Spain for the period 2016 to 2020. Subsequently, predictions for the year 2021 were made and compared to the actual crash occurrences during that year. The results indicate that the best model is the one that combines the macroscopic and microscopic levels using the approaches AHP+NB+GWPR, which presents a Mean Absolute Deviation (MAD) of 1.55, a Mean Square Error (MSE) of 2.89, does not underestimate any segments and shows a recall of 0.88. The results obtained confirm that the combination of models at the macroscopic and microscopic levels yields the best crashes predictions. This approach offers the advantage that our model, in addition to learning from historical data using statistical methods, also fits to the importance weights assigned by knowledgeable experts on the crashes propensity of the highway in the case study area using the Analytical Hierarchical Process.

Using Perceptual Cycle Model and Text Mining to Investigate Ambulance Traffic Crashes

Ambulance crashes constitute a matter of utmost concern within public health, posing potential risks to both patients and emergency responders. Despite this critical importance, investigating the underlying causes of these collisions is difficult because of the scarcity of comprehensive and relevant datasets. To bridge this research gap and gain valuable insights, the present study embarked on a mission to shed light on the causative factors behind ambulance-related crashes. To achieve this objective, this study adopted a meticulous approach, collecting narrative descriptions from ten special investigation reports published by the National Highway Traffic Safety Administration. These reports were selected as they offered in-depth accounts of real-life ambulance crashes, rendering them an invaluable resource for analyzing the multifaceted aspects leading to such incidents. Central to this investigation was the utilization of the Perceptual Cycle Model (PCM), a well-established and comprehensive framework that facilitates a systematic examination of the various stages leading to a crash. The study examined the key influential factors associated with ambulance crashes by employing PCM and text mining. The results reveal diverse factors contributing to ambulance crashes, including varied causes, driver actions, and post-crash scenarios, providing a holistic understanding of road safety. The outcomes of this study will bolster the safety of ambulance operations, safeguard patients and personnel, and ensure the efficient delivery of life-saving emergency services to those in need.

Epidemiological characteristics of traumatic spinal fractures among the elderly in China

The exploration of traumatic spinal fractures (TSFs) within the senior demographic has not been thoroughly scrutinized, particularly with respect to variations across genders, age groups, seasonal periods, and causative factors. This retrospective analysis aimed to dissect differences in the prevalence and characteristics of TSFs among the elderly, factoring in gender, age, seasonal timing, and causation. A retrospective analysis was conducted on the medical and imaging records of 1,415 patients, all aged 60 years or older, who were diagnosed with TSFs from 2013 to 2019. This study categorized the data by gender, age groups (60–70, 70–80, and 80 years or older), seasons, and the cause of injuries, including road traffic crashes (RTCs), falls from low heights (LHF), falls from high heights (HHF), and injuries incurred during everyday activities and agricultural labor (DFI). Male patients exhibited notably higher incidences of RTCs, high-height falls (HHFs), outdoor incidents, comas post-injury, fractures of the lower limbs (LLFs), pelvic fractures (PFs), rib fractures (RFs), intra-thoracic injuries (ITIs), intra-abdominal injuries (IAIs), cervical fractures, and spinal cord injuries (SCIs). With advancing age, there was a marked decline in occurrences of RTCs, HHFs, outdoor incidents, RFs, craniocerebral injuries (CCIs), ITIs, cervical fractures, and SCIs, while the incidences of DFIs, indoor incidents, and thoracic and lumbar (T + L) fractures notably increased. During autumn, LLF occurrences were significantly reduced, whereas the winter season saw an increase in thoracic fractures. Spring time was associated with a higher frequency of lumbar fractures and noncontiguous spinal fractures (NSFs). Significant distinctions were observed in the age distribution, injury circumstances, associated injuries, and SCIs between high-energy impacts (RTCs and HHFs) and low-energy traumas (LHFs and DFIs). In the elderly demographic, TSFs exhibited discernible distinctions based on gender, age, seasonal variations, and etiological factors, impacting the nature and circumstances of injuries, associated traumas, complications, fracture sites, and the occurrence of SCIs.

Anterior odontoid screw fixation of a type III odontoid fracture aided by transoral digital manipulation: illustrative case.

Odontoid process fractures make up 10%-20% of all cervical spine fractures, with type III fractures having a considerable amount of heterogeneity. Most simple type III fractures can be managed nonoperatively. However, 21% of complex type III fractures with significant displacement and angulation are inadequately treated with external immobilization and require surgery. Achieving a reduction via traction can pose a great challenge during intraoperative manipulation, especially when presentation is delayed. A 36-year-old male patient, who presented 2 weeks after a motor vehicle crash, had a Glasgow Coma Scale score of 14 and intact motor and sensory function. A craniocervical computed tomograpy scan revealed a normal brain with a type III odontoid fracture. The patient underwent anterior odontoid screw fixation (AOSF) aided by a transoral digital manipulation to achieve a reduction of the irreducible proximal fracture segment at 8 weeks postinjury. The treatment resulted in preserved neurological function and a satisfactory odontoid fracture reduction. Delayed presentation of a complex type III odontoid fracture can be challenging to treat; however, AOSF with the aid of transoral digital manipulation of the irreducible proximal segment can help to achieve good reduction and fusion with the preservation of neurological function in a young patient. https://thejns.org/doi/10.3171/CASE24294.